Landing a machine learning engineer job takes more than just learning Python. In this video, I break down what actually moves the needle: mindset, a realistic learning path, and a job-search strategy that matches today’s market. I start with what I call a fearless mindset, because most people don’t fail from lack of talent—they get stuck in fear stories like “I’m too old,” “I didn’t go to the right school,” or “I’m not smart enough.” I’ve been in tech for 20 years (including being the 19th engineer at WhatsApp and a former Meta hiring manager), and I’ve seen career pivots work over and over. Learning is a skill, not a talent—and consistency beats “genius.” Then I cover three ways to build ML skills: university, bootcamps, and self-study. Degrees can be structured but expensive, and unless you’re in a top program there’s no guarantee you’ll get hired—plus the landscape can shift by graduation. Bootcamps can be faster and supportive, but they’re also pricey and the market is more competitive post-2023 layoffs, so you need to check third-party outcomes (like CIRR). Self-study is the most cost-effective, but it requires discipline and accountability. Finally, I talk strategy: interviewing is its own skill, so practice (LeetCode, mock interviews, feedback) and use community to stay accountable—ideally in a small group where you practice communicating your thinking clearly.